mirror of
https://github.com/DOI-DO/j40-cejst-2.git
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6f39033dde
* Add a rough prototype allowing a developer to pre-download data sources for all ETLs * Update code to be more production-ish * Move fetch to Extract part of ETL * Create a downloader to house all downloading operations * Remove unnecessary "name" in data source * Format source files with black * Fix issues from pylint and get the tests working with the new folder structure * Clean up files with black * Fix unzip test * Add caching notes to README * Fix tests (linting and case sensitivity bug) * Address PR comments and add API keys for census where missing * Merging comparator changes from main into this branch for the sake of the PR * Add note on using cache (-u) during pipeline
127 lines
4.6 KiB
Python
127 lines
4.6 KiB
Python
from glob import glob
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import geopandas as gpd
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import pandas as pd
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from data_pipeline.config import settings
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from data_pipeline.etl.base import ExtractTransformLoad
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from data_pipeline.etl.datasource import DataSource
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from data_pipeline.etl.datasource import ZIPDataSource
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from data_pipeline.score import field_names
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from data_pipeline.utils import get_module_logger
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logger = get_module_logger(__name__)
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class MarylandEJScreenETL(ExtractTransformLoad):
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"""Maryland EJSCREEN class that ingests dataset represented
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here: https://p1.cgis.umd.edu/mdejscreen/help.html
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Please see the README in this module for further details.
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"""
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def __init__(self):
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# fetch
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self.maryland_ejscreen_url = (
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settings.AWS_JUSTICE40_DATASOURCES_URL + "/MD_EJScreen.zip"
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)
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# input
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self.shape_files_source = self.get_sources_path() / "mdejscreen"
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# output
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self.OUTPUT_CSV_PATH = self.DATA_PATH / "dataset" / "maryland_ejscreen"
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self.COLUMNS_TO_KEEP = [
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self.GEOID_TRACT_FIELD_NAME,
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD,
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field_names.MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD,
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]
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self.df: pd.DataFrame
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self.dfs_list: pd.DataFrame
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def get_data_sources(self) -> [DataSource]:
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return [
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ZIPDataSource(
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source=self.maryland_ejscreen_url,
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destination=self.get_sources_path(),
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)
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]
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def extract(self, use_cached_data_sources: bool = False) -> None:
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super().extract(
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use_cached_data_sources
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) # download and extract data sources
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logger.debug("Downloading 207MB Maryland EJSCREEN Data")
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list_of_files = list(glob(str(self.shape_files_source) + "/*.shp"))
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# Ignore counties because this is not the level of measurement
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# that is consistent with our current scoring and ranking methodology.
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self.dfs_list = [
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gpd.read_file(f)
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for f in list_of_files
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if not f.endswith("CountiesEJScore.shp")
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]
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def transform(self) -> None:
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# Set the Census tract as the index and drop the geometry column
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# that produces the census tract boundaries.
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# The latter is because Geopandas raises an exception if there
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# are duplicate geometry columns.
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# Moreover, since the unit of measurement is at the tract level
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# we can consistantly merge this with other datasets
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self.dfs_list = [
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df.set_index("Census_Tra").drop("geometry", axis=1)
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for df in self.dfs_list
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]
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# pylint: disable=unsubscriptable-object
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self.df = gpd.GeoDataFrame(pd.concat(self.dfs_list, axis=1))
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# Reset index so that we no longer have the tract as our index
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self.df = self.df.reset_index()
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# coerce GEODID into integer
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# The only reason why this is done is because Maryland's GEODID's start with
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# "24". This is NOT standard practice and should never be done as rightly pointed
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# out by Lucas: "converting to int would lose the leading 0 and make this geoid invalid".
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# pylint: disable=unsupported-assignment-operation, unsubscriptable-object
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self.df["Census_Tra"] = (self.df["Census_Tra"]).astype(int)
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# Drop the 10 census tracts that are zero: please see here:
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# https://github.com/usds/justice40-tool/issues/239#issuecomment-995821572
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self.df = self.df[self.df["Census_Tra"] != 0]
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# Rename columns
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self.df.rename(
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columns={
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"Census_Tra": self.GEOID_TRACT_FIELD_NAME,
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"EJScore": field_names.MARYLAND_EJSCREEN_SCORE_FIELD,
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},
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inplace=True,
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)
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# This computational step will be used to establish a
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# threshold for burden (line 104)
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self.df[
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD
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+ field_names.PERCENTILE_FIELD_SUFFIX
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] = self.df[field_names.MARYLAND_EJSCREEN_SCORE_FIELD].rank(
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pct=True, ascending=True
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)
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# An arbitrarily chosen threshold is used in the comparison tool output
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self.df[field_names.MARYLAND_EJSCREEN_BURDENED_THRESHOLD_FIELD] = (
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self.df[
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field_names.MARYLAND_EJSCREEN_SCORE_FIELD
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+ field_names.PERCENTILE_FIELD_SUFFIX
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]
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>= 0.75
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)
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def load(self) -> None:
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# write maryland tracts to csv
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self.OUTPUT_CSV_PATH.mkdir(parents=True, exist_ok=True)
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self.df[self.COLUMNS_TO_KEEP].to_csv(
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self.OUTPUT_CSV_PATH / "maryland.csv", index=False
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)
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